Computer-aided Vaccine Design Research Articles

Computer-Aided vaccine design for selected positive-sense single stranded RNA viruses

Spontaneous mutations and lack of replication fidelity in positive-sense single stranded RNA viruses (+ssRNA virus) result in emergence of genetic variants with diverse viral morphogenesis and surface proteins that affect its antigenicity. This high mutability in +ssRNA viruses has induced antiviral drug resistance and ability to overcome vaccines that subsequently resulted in rapid viral evolution and high mortality rate in human and livestock. Computer aided vaccine design and immunoinformatics play a crucial role in expediting the vaccine production protocols, antibody production and identifying suitable immunogenic regions or epitopes from the genome sequences of the pathogens. T cell and B cell epitopes can be identified in pathogens by immunoinformatics algorithms and methods that enhance the analysis of protective immunity, vaccine safety, immunity modelling and vaccine efficacy. This rapid and cost-effective computational vaccine design promotes development of potential vaccine that could induce immune response in host against rapidly mutating pathogens like +ssRNA viruses. Epitope-based vaccine is a striking concept that has been widely employed in recent years to construct vaccines targeting rapidly mutating +ssRNA viruses. Therefore, the present review provides an overview about the current progress and methodology in computer-aided vaccine design for the most notable +ssRNA viruses namely Hepatitis C virus, Dengue virus, Chikungunya virus and Coronaviruses. This review also highlights the applications of various immunoinformatics tools for vaccine design and for modelling immune response against +ssRNA viruses.

Towards the first multi-epitope recombinant vaccine against Crimean-Congo hemorrhagic fever virus: A computer-aided vaccine design approach.

Crimean-Congo hemorrhagic fever (CCHF) is considered one of the major public health concerns with case fatality rates of up to 80%. Currently, there is no effective approved vaccine for CCHF. In this study, we used a computer-aided vaccine design approach to develop the first multi-epitope recombinant vaccine for CCHF. For this purpose, linear B-cell and T-cell binding epitopes from two structural glycoproteins of CCHF virus including Gc and Gn were predicted. The epitopes were further studied regarding their antigenicity, allergenicity, hydrophobicity, stability, toxicity and population coverage. A total number of seven epitopes including five T-cell and two B-cell epitopes were screened for the final vaccine construct. Final vaccine construct composed of 382 amino acid residues which were organized in four domains including linear B-cell, T-cell epitopes and cholera toxin B-subunit (CTxB) along with heat labile enterotoxin IIc B subunit (LT-IIc) as adjuvants. All the segments were joined using appropriate linkers. The physicochemical properties as well as the presence of IFN-γ inducing epitopes in the proposed vaccine, was also checked to determining the vaccine stability, solubility and its ability to induce cell-mediated immune responses. The 3D structure of proposed vaccine was subjected to the prediction of computational B-cell epitopes and molecular docking studies with MHC-I and II molecules. Furthermore, molecular dynamics stimulations were performed to study the vaccine-MHCs complexes stability during stimulation time. The results suggest that our proposed vaccine was stable, well soluble in water and potentially antigenic. Results also demonstrated that the vaccine can induce both humoral and cell-mediated immune responses and could serve as a promising anti-CCHF vaccine candidate.

Broad spectrum assessment of the epitope fluctuation—Immunogenicity hypothesis

Prediction of immunogenicity is a substantial barrier in vaccine design. Here, a molecular dynamics approach to assessing the immunogenicity of nanoparticles based on structure is presented. Molecular properties of epitopes on nonenveloped viral particles are quantified via a set of metrics. One such metric, epitope fluctuation (and implied flexibility), is shown to be inversely correlated with immunogenicity for each of a broad spectrum of nonenveloped viruses. The molecular metrics and experimentally determined immunogenicities for these viruses are archived in the open-source vaccine computer-aided design database. Results indicate the promise of computer-aided vaccine design to bring greater efficiency to traditional lab-based vaccine discovery approaches.

Epitope engineering and molecular metrics of immunogenicity: A computational approach to VLP-based vaccine design

Developing antiviral vaccines is increasingly challenging due to associated time and cost of production as well as emerging drug-resistant strains. A computer-aided vaccine design strategy is presented that could greatly accelerate the discovery process and yield vaccines with high immunogenicity and thermal stability. Our strategy is based on foreign viral epitopes engineered onto well-established virus-like particles (VLPs) and demonstrates that such constructs present similar affinity for antibodies as does a native virus. This binding affinity serves as one molecular metric of immunogenicity. As a demonstration, we engineered a preS1 epitope of hepatitis B virus (HBV) onto the EF loop of human papillomavirus VLP (HPV-VLP). HBV-associated HzKR127 antibody displayed binding affinity for this structure at distances and strengths similar to those for the complex of the antibody with the full HBV (PDBID: 2EH8). This antibody binding affinity assessment, along with other molecular immunogenicity metrics, could be a key component of a computer-aided vaccine design strategy.

A molecular dynamics study of loop fluctuation in human papillomavirus type 16 virus-like particles: A possible indicator of immunogenicity

Immunogenicity varies between the human papillomavirus (HPV) L1 monomer assemblies of various sizes (e.g., monomers, pentamers or whole capsids). The hypothesis that this can be attributed to the intensity of fluctuations of important loops containing neutralizing epitopes for the various assemblies is proposed for HPV L1 assemblies. Molecular dynamics simulations were utilized to begin testing this hypothesis. Fluctuations of loops that contain critical neutralizing epitopes (especially FG loop) were quantified via root-mean-square fluctuation and features in the frequency spectrum of dynamic changes in loop conformation. If this fluctuation-immunogenicity hypothesis is a universal aspect of immunogenicity (i.e., immune system recognition of an epitope within a loop is more reliable when it is presented via a more stable delivery structure), then fluctuation measures can serve as one predictor of immunogenicity as part of a computer-aided vaccine design strategy.

Gaussian Process: A Promising Approach for the Modeling and Prediction of Peptide Binding Affinity to MHC Proteins

On the basis of Bayesian probabilistic inference, Gaussian process (GP) is a powerful machine learning method for nonlinear classification and regression, but has only very limited applications in the new areas of computational vaccinology and immunoinformatics. In the current work, we present a paradigmatic study of using GP regression technique to quantitatively model and predict the binding affinities of over 7000 immunodominant peptide epitopes to six types of human major histocompatibility complex (MHC) proteins. In this procedure, the sequence patterns of diverse peptides are characterized quantitatively and the resulting variables are then correlated with the experimentally measured affinities between different MHC and their peptide ligands, by using a linearity- and nonlinearity-hybrid GP approach. We also make systematical comparisons between the GP and two sophisticated modeling methods as partial least square (PLS) regression and support vector machine (SVM) with respect to their fitting ability, predictive power and generalization capability. The results suggest that GP could be a new and effective tool for the modeling and prediction of MHC-peptide interactions and would be promising in the field of computer-aided vaccine design (CAVD).

Order Parameters for Multiscale Simulation of Bio-Nanosystems

Order Parameters (OPs) characterizing the structure and organization of bio-nanosystems (BNSs) are presented. Deductive all-atom multiscale techniques imply that structural transitions in several BNSs can be simulated via the slow, temporal OP dynamics, which co-evolves with a quasi-equilibrium probability density for rapidly fluctuating atomic configurations. This yields a force-field based algorithm that allows for all-atom BNS simulations with high CPU efficiency. Salient features of a set of OPs including their construction, emergence and dynamical completeness are discussed. These are critical in probing the free-energy landscapes underlying structural transitions. The computational algorithm is implemented via a software, denoted SimNanoWorld, that we demonstrate in applications to macromolecules and macromolecular assemblies. This includes simulating the dynamics of viral RNA and RNA-protein complexes in Satellite Tobacco Mosaic Virus (STMV) over a range of electrolytes (1:1 and 2:1) and temperatures (300K-600K). Another example demonstrates the application of OPs in probing the stability and immunogenicity of a T =1 Human Papillomavirus (HPV) L1-protein Virus-like Particle (VLP). Prospects of computer-aided vaccine design will be discussed.

Computer-aided vaccine design for liver cancer using epitopes of HBx protein isolates from HBV substrains

Hepatitis B Virus encodes HBX, which is considered to be responsible for virus infection in mammals. The HBx modulates the signal transduction pathways toward the aetiological aspects of establishing liver cancer. In this context, to predict epitopes from three HBX protein (for vaccine designs), use of certain computational tools are indicated. Relevant study shows that seven binding peptides for MHC class I and 16 for MHC class II molecule bear significant binding affinity. Epitope LSAMSTTDL for MHC I and LRFTSARRM, HLSLRGLPV for MHC II have highest log-values of 0.746 and (0.8628, 0.8048), respectively. Vaccine model is designed using predicted epitopes. Such patterns characterise possible biological activity of the vaccine of interest.

Enveloped viruses understood via multiscale simulation: computer-aided vaccine design

Enveloped viruses are viewed as an opportunity to understand how highly organized and functional biosystems can emerge from a collection of millions of chaotically moving atoms. They are an intermediate level of complexity between macromolecules and bacteria. They are a natural system for testing theories of self-assembly and structural transitions, and for demonstrating the derivation of principles of microbiology from laws of molecular physics. As some constitute threats to human health, a computer-aided vaccine and drug design strategy that would follow from a quantitative model would be an important contribution. However, current molecular dynamics simulation approaches are not practical for modeling such systems. Our multiscale approach simultaneously accounts for the outer protein net and inner protein/genomic core, and their less structured membranous material and host fluid. It follows from a rigorous multiscale deductive analysis of laws of molecular physics. Two types of order parameters are introduced: (1) those for structures wherein constituent molecules retain long-lived connectivity (they specify the nanoscale structure as a deformation from a reference configuration) and (2) those for which there is no connectivity but organization is maintained on the average (they are field variables such as mass density or measures of preferred orientation). Rigorous multiscale techniques are used to derive equations for the order parameters dynamics. The equations account for thermal-average forces, diffusion coefficients, and effects of random forces. Statistical properties of the atomic-scale fluctuations and the order parameters are co-evolved. By combining rigorous multiscale techniques and modern supercomputing, systems of extreme complexity can be modeled.

Using databases and data mining in vaccinology

Throughout time functional immunology has accumulated vast amounts of quantitative and qualitative data relevant to the design and discovery of vaccines. Such data includes, but is not limited to, components of the host and pathogen genome (including antigens and virulence factors), T- and B-cell epitopes and other components of the antigen presentation pathway and allergens. In this review the authors discuss a range of databases that archive such data. Built on such information, increasingly sophisticated data mining techniques have developed that create predictive models of utilitarian value. With special reference to epitope data, the authors discuss the strengths and weaknesses of the available techniques and how they can aid computer-aided vaccine design deliver added value for vaccinology.